{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import layers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   3.44827586,   6.89655172,  10.34482759,\n",
       "        13.79310345,  17.24137931,  20.68965517,  24.13793103,\n",
       "        27.5862069 ,  31.03448276,  34.48275862,  37.93103448,\n",
       "        41.37931034,  44.82758621,  48.27586207,  51.72413793,\n",
       "        55.17241379,  58.62068966,  62.06896552,  65.51724138,\n",
       "        68.96551724,  72.4137931 ,  75.86206897,  79.31034483,\n",
       "        82.75862069,  86.20689655,  89.65517241,  93.10344828,\n",
       "        96.55172414, 100.        ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0,100,30)\n",
    "# 标准正态分布的随机数 np.random.randn(30), 30 个\n",
    "y = 3 * x + 7 + np.random.randn(30) * 10\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-10.63154241,   1.94462679,  53.03782678,  25.26392488,\n",
       "        49.11118229,  79.38321786,  69.83442458,  66.07111105,\n",
       "        92.00493798, 103.22539935, 105.50592751, 105.96565158,\n",
       "       129.9209293 , 137.43931934, 144.58413939, 171.37771751,\n",
       "       164.92331154, 207.65966241, 182.52157474, 202.24404688,\n",
       "       211.05832244, 236.56304465, 221.87657647, 238.65111755,\n",
       "       249.85937575, 274.06690279, 269.77961307, 299.66485609,\n",
       "       290.68682676, 342.94803161])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x288f3299348>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 顺序模型\n",
    "model = keras.Sequential() \n",
    "model.add(layers.Dense(1,input_dim=1))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 编译模型\n",
    "model.compile(optimizer='adam',loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 30 samples\n",
      "Epoch 1/3000\n",
      "30/30 [==============================] - 0s 248us/sample - loss: 10741.5137\n",
      "Epoch 2/3000\n",
      "30/30 [==============================] - 0s 0s/sample - loss: 10732.9023\n",
      "Epoch 3/3000\n",
      "30/30 [==============================] - 0s 119us/sample - loss: 10724.2959\n",
      "Epoch 4/3000\n",
      "30/30 [==============================] - 0s 106us/sample - loss: 10715.6934\n",
      "Epoch 5/3000\n",
      "30/30 [==============================] - 0s 116us/sample - loss: 10707.0957\n",
      "Epoch 6/3000\n",
      "30/30 [==============================] - 0s 81us/sample - loss: 10698.5049\n",
      "Epoch 7/3000\n",
      "30/30 [==============================] - 0s 66us/sample - loss: 10689.9170\n",
      "Epoch 8/3000\n",
      "30/30 [==============================] - 0s 57us/sample - loss: 10681.3340\n",
      "Epoch 9/3000\n",
      "30/30 [==============================] - 0s 88us/sample - loss: 10672.7559\n",
      "Epoch 10/3000\n",
      "30/30 [==============================] - 0s 66us/sample - loss: 10664.1826\n",
      "Epoch 11/3000\n",
      "30/30 [==============================] - 0s 77us/sample - loss: 10655.6123\n",
      "Epoch 12/3000\n",
      "30/30 [==============================] - 0s 72us/sample - loss: 10647.0498\n",
      "Epoch 13/3000\n",
      "30/30 [==============================] - 0s 88us/sample - loss: 10638.4902\n",
      "Epoch 14/3000\n",
      "30/30 [==============================] - 0s 88us/sample - loss: 10629.9365\n",
      "Epoch 15/3000\n",
      "30/30 [==============================] - 0s 66us/sample - loss: 10621.3857\n",
      "Epoch 16/3000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 10612.8418\n",
      "Epoch 17/3000\n",
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
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     ]
    },
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    },
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     ]
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    },
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     ]
    },
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     "text": [
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    },
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     ]
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     ]
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     ]
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     ]
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     ]
    },
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     "text": [
      "Epoch 2018/3000\n",
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     ]
    },
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     "text": [
      "Epoch 2109/3000\n",
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    },
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     ]
    },
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     ]
    },
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     ]
    },
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     "text": [
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     ]
    },
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     ]
    },
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     "text": [
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     ]
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     ]
    },
    {
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     "text": [
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     ]
    },
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     "text": [
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      "30/30 [==============================] - 0s 53us/sample - loss: 316.6519\n",
      "Epoch 3000/3000\n",
      "30/30 [==============================] - 0s 77us/sample - loss: 316.2774\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x288f390e588>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练模型\n",
    "model.fit(x, y, epochs = 3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.0730267],\n",
       "       [ 13.936321 ],\n",
       "       [ 23.799616 ],\n",
       "       [ 33.66291  ],\n",
       "       [ 43.526207 ],\n",
       "       [ 53.389503 ],\n",
       "       [ 63.2528   ],\n",
       "       [ 73.1161   ],\n",
       "       [ 82.979385 ],\n",
       "       [ 92.84269  ],\n",
       "       [102.70598  ],\n",
       "       [112.569275 ],\n",
       "       [122.43257  ],\n",
       "       [132.29587  ],\n",
       "       [142.15916  ],\n",
       "       [152.02245  ],\n",
       "       [161.88574  ],\n",
       "       [171.74905  ],\n",
       "       [181.61235  ],\n",
       "       [191.47565  ],\n",
       "       [201.33893  ],\n",
       "       [211.20224  ],\n",
       "       [221.06552  ],\n",
       "       [230.92883  ],\n",
       "       [240.79211  ],\n",
       "       [250.6554   ],\n",
       "       [260.5187   ],\n",
       "       [270.382    ],\n",
       "       [280.2453   ],\n",
       "       [290.10858  ]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x288f3a4f588>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,c = 'r')\n",
    "plt.plot(x, model.predict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[433.12637]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测应用\n",
    "model.predict([150])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
